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Over the past couple of years, cross platform desktop app development has been getting a lot of attention. Developers want to build apps that can be used across multiple platforms, like Android, iOS, and Windows. It’s a trend that’s been gaining momentum over the past few years, and there are plenty of tools and frameworks available to help you do this. Many have turned to third-party libraries, tools, and other ready-made solutions to help with their cross platform development needs. However, there is always a possibility for some drawbacks. With this in mind, we’ve decided to put together some tips you can consider for a project involving cross platform desktop app development. First, though, let’s start with the basics. What is cross platform desktop app development? Cross platform desktop app development involves the use of different frameworks and libraries to develop a native app that can be used across multiple platforms. With cross platform native desktop app development, you can develop your app for the Windows, Mac, Linux, and Android platforms all at once. The primary benefit of this is that the code is shared between all of these platforms in a single codebase. This makes it easier to manage your code and save time when you are building your app, as you don’t have to pay for each platform’s native SDK or create a separate version of the app. However, with cross platform native desktop app development comes certain challenges. One main drawback that many developers come across is that they have to sacrifice a lower performance or user experience to compensate for cutting back on time and cost. Still, there are many benefits to using cross platform native desktop app development. By using the right tools, you can get a wider range of users and expand your reach across all the platforms that you support with little fuss. What are the challenges of cross platform desktop app development? When you start building a cross platform native desktop app, there are a few challenges that you should be aware of. Here are some of the most common ones: Errors when transferring codes. Trying to transfer your code to different platforms can cause errors. Sometimes, it may even lead to partial loss of your codes. A code for a certain platform may not always be compatible with another platform. If you’re using a third-party library or app builder software, you need to make sure that it’s compatible with the other platforms. Some libraries may be not as stable as others, which can cause issues when transferring your code to the different platforms. Testing your app on all platforms simultaneously. You’ll need to test your app on all the different platforms at once, which is time consuming and may take up more time than expected. You also have to ensure that everything works correctly before going live with your app on the different platforms, as this can save a lot of time and money in the long run. Non-compatible languages for each platform. Not all languages are compatible with each other, which can cause errors when trying to transfer codes or creating new apps from scratch for each platform individually. The problem is even worse if you’re working with multiple languages simultaneously like JavaScript and Python or Java and C++ at […]

These days, many app developers are looking into building their mobile apps to cater to multiple platforms. It is essential to create an app that will be able to operate on multiple platforms like Android, iOS, Windows and so on. Creating cross platform apps can be a very challenging process, especially for mobile devices, but it can bring many advantages. Despite the challenges, picking the right software development platform, especially one which has a low code ideology such as RAD Studio Delphi, is not an insurmountable hill to climb. The right IDE software mitigates a lot of the difficulty and helps you create apps with the minimum of required effort from you and with the maximum efficiency. Using a highly capable tool such as RAD Studio and the Firemonkey FMX framework for cross platform apps means you can focus on one code base for your app even if it’s going to run on multiple mobile targets such as iOS and Android. This guide will give you some pointers in creating a cross-platform app that will be able to run on multiple platforms. You will also learn how to make your app highly efficient, which is an essential part of mobile app development. Working smarter, not harder is the whole philosophy behind RAD Studio Delphi. Let’s start by discussing what building cross platform apps actually entail. What do we mean by development of cross platform apps? Cross-platform development is a process that will allow you to create an app that will be able to run on multiple platforms. The most common examples of cross-platform apps are mobile apps, which are basically software applications designed to run on mobile devices. This is a direct opposition to proprietary app development which is developed specifically to only run on a single platform. These kinds of apps often use a technology which is supplied by the hardware manufacturer and can lock the developer into that mobile hardware vendor’s ‘ecosystem’ where they provide the development tools, the hardware on which you must develop and even the app store through which you must distribute your app. These vertical development solutions can seem attractive since the vendor often streamlines the process to market – as long as that market is the one they exclusively control. Going down that route can often mean inadvertently creating cross platform apps which cannot actually work on another hardware platform without substantial rewrites, sometimes total re-engineering of the solution. Meanwhile, the idea behind genuine cross-platform development is to save developers a lot of time and effort while providing them with the most options for expansion across different hardware vendors. It will also allow developers to market their apps in a more efficient way, as they can reach out to a wider pool of potential users. Ideally, you can minimize the effort it takes to manage code while simultaneously cutting back on costs, since you’d only need one development team. Picking a tool which maximizes the idea of a single code base for the app, with minimal changes required to target another platform, is the smart choice. What is the secret to efficiently targeting multiple device types with mobile cross platform apps? What’s the secret? Choosing the right cross platform development frameworks. These are the building blocks that will help you develop your app […]

Machine Learning is one of the hottest software development topics right now. The algorithms and techniques which enable machine learning have begun to really mature and have graduated from ‘interesting ideas’ into providing genuine power and permitting capabilities in our apps which can sometimes seem magical just as much as they are useful. Python has very quickly emerged as a de facto language for machine learning. There is rich set of machine learning libraries available for Python providing the ability to do everything from image recognition to complicated scientific analyses. How to use machine learning libraries to Delphi Did you know that it’s simple to use some truly excellent Python libraries to boost your Delphi app development on Windows? All that wonderful array of Python machine learning goodness is also easily available to you as a Delphi programmer. Adding Python to your Delphi code toolbox can enhance your app development by bringing in new capabilities that allow you to provide innovative and powerful solutions to your app’s users, combining the best of Python with the supreme low-code and unparalleled power of native Windows development that Delphi provides. Are you looking for how to build a GUI for a powerful Unsupervised Machine Learning library?  You can build a state-of-the-art unsupervised learning solution with scikit-learn on Delphi. This article will demonstrate how to create a Delphi GUI app dedicated to the scikit-learn library. Watch this video by Jim McKeeth for a thorough explanation of why you can love both Delphi and Python at the same time: What is the scikit-learn machine learning library? scikit-learn logo (source: scikit-learn.org). scikit-learn is an open-source Python machine learning library. scikit-learn has simple and efficient tools for predictive data analysis that are built on top of SciPy, NumPy, and Matplotlib. Support vector machines, random forests, gradient boosting, k-means, and DBSCAN are among the algorithms available in scikit-learn for classification, regression, and clustering. In this article, we will specifically talk about clustering algorithms. What is unsupervised machine learning? Unsupervised learning, also known as unsupervised machine learning, analyzes and clusters unlabeled datasets using machine learning algorithms. Without human intervention, these algorithms uncover hidden patterns or data groupings. Its ability to detect similarities and differences in data makes it an ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition (source: IBM Cloud Education, 2020). What is clustering and how does it relate to machine learning? Clustering is a type of unsupervised learning problem. Cluster analysis is another name for this technique. It is frequently used as a data analysis technique for discovering interesting patterns in data, such as customer groups based on their behavior. There are numerous clustering algorithms available, and there is no single best clustering algorithm for all cases. Instead, it is a good idea to experiment with various clustering algorithms and different configurations for each algorithm. A cluster is frequently a dense area in the feature space where examples from the domain (observations or rows of data) are closer to the cluster than to other clusters. The cluster may have a sample or point feature space as its center (the centroid), as well as a boundary or extent (source: Brownlee, machinelearningmastery.com, 2020). A comparison of the clustering algorithms in scikit-learn (source: scikit-learn.org). What is the difference between supervised and unsupervised machine learning? The following infographic created by AltexSoft may […]

Text is everywhere! Text is not always where we want it, or in the format we need. We often find ourselves needing the text from images. You might have a scanned document containing financial information like an invoice you want to input into an accounts system, or maybe you are building an archive of photographs that you want to index against any text detected? Whatever your use case, if you need to get some text from an image, you can achieve this with ease in Delphi programming software using Amazon Textract using the latest version of Appercept AWS SDK for Delphi. What is Amazon Textract and what does it do? Amazon Textract provides text detection and analysis for image documents. Textract can analyse the relationships between detected text objects and provide information relating to forms, financial, and identity documents. Let’s detect some text… program DetectText; {$APPTYPE CONSOLE} implementation uses AWS.Textract; var Client: ITextractClient; Request: ITextractDetectDocumentTextRequest; Response: ITextractDetectDocumentTextResponse; begin Request := TTextractDetectDocumentTextRequest.Create; Request.Document := TTextractDocument.FromFile(‘C:image.png’); Client := TTextractClient.Create; Response := Client.DetectDocumentText(Request); if Response.IsSuccessful then begin for var LBlock in Response.Blocks do WriteLn(Format(‘Detected %s: %s’, [LBlock.BlockType, LBlock.Text])); end; end. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 program DetectText;   {$APPTYPE CONSOLE}   implementation   uses   AWS.Textract;   var   Client: ITextractClient;   Request: ITextractDetectDocumentTextRequest;   Response: ITextractDetectDocumentTextResponse;   begin   Request := TTextractDetectDocumentTextRequest.Create;   Request.Document := TTextractDocument.FromFile(‘C:image.png’);     Client := TTextractClient.Create;   Response := Client.DetectDocumentText(Request);   if Response.IsSuccessful then   begin     for var LBlock in Response.Blocks do       WriteLn(Format(‘Detected %s: %s’, [LBlock.BlockType, LBlock.Text]));   end; end. Is there an example of using Amazon Textract? To help you experiment and get started, check out the Textract demo in the AWS SDK for Delphi Samples repository. About Appercept AWS SDK for Delphi Appercept AWS SDK for Delphi is available exclusively on GetIt with active Enterprise or Architect subscriptions for Embarcadero Delphi or RAD Studio. You can install the SDK through the GetIt Package Manager within Delphi or RAD Studio if you have an active subscription.